simple apis now cited/context doc indices

backend/danswer/chat/models.py

@@ -60,7 +60,11 @@ def model_dump(self, *args: list, **kwargs: dict[str, Any]) -> dict[str, Any]:
 
 
 class LLMRelevanceFilterResponse(BaseModel):
-    relevant_chunk_indices: list[int]
+    llm_selected_doc_indices: list[int]
+
+
+class FinalUsedContextDocsResponse(BaseModel):
+    final_context_docs: list[LlmDoc]
 
 
 class RelevanceAnalysis(BaseModel):
@@ -93,6 +97,16 @@ class CitationInfo(BaseModel):
     document_id: str
 
 
+class AllCitations(BaseModel):
+    citations: list[CitationInfo]
+
+
+# This is a mapping of the citation number to the document index within
+# the result search doc set
+class MessageSpecificCitations(BaseModel):
+    citation_map: dict[int, int]
+
+
 class MessageResponseIDInfo(BaseModel):
     user_message_id: int | None
     reserved_assistant_message_id: int
@@ -138,7 +152,7 @@ class QAResponse(SearchResponse, DanswerAnswer):
     predicted_flow: QueryFlow
     predicted_search: SearchType
     eval_res_valid: bool | None = None
-    llm_chunks_indices: list[int] | None = None
+    llm_selected_doc_indices: list[int] | None = None
     error_msg: str | None = None
 
 

backend/danswer/chat/process_message.py

@@ -7,12 +7,15 @@
 from sqlalchemy.orm import Session
 
 from danswer.chat.chat_utils import create_chat_chain
+from danswer.chat.models import AllCitations
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import CustomToolResponse
 from danswer.chat.models import DanswerAnswerPiece
+from danswer.chat.models import FinalUsedContextDocsResponse
 from danswer.chat.models import ImageGenerationDisplay
 from danswer.chat.models import LLMRelevanceFilterResponse
 from danswer.chat.models import MessageResponseIDInfo
+from danswer.chat.models import MessageSpecificCitations
 from danswer.chat.models import QADocsResponse
 from danswer.chat.models import StreamingError
 from danswer.configs.chat_configs import BING_API_KEY
@@ -85,6 +88,7 @@
 )
 from danswer.tools.internet_search.internet_search_tool import InternetSearchResponse
 from danswer.tools.internet_search.internet_search_tool import InternetSearchTool
+from danswer.tools.search.search_tool import FINAL_CONTEXT_DOCUMENTS_ID
 from danswer.tools.search.search_tool import SEARCH_RESPONSE_SUMMARY_ID
 from danswer.tools.search.search_tool import SearchResponseSummary
 from danswer.tools.search.search_tool import SearchTool
@@ -100,9 +104,9 @@
 logger = setup_logger()
 
 
-def translate_citations(
+def _translate_citations(
     citations_list: list[CitationInfo], db_docs: list[DbSearchDoc]
-) -> dict[int, int]:
+) -> MessageSpecificCitations:
     """Always cites the first instance of the document_id, assumes the db_docs
     are sorted in the order displayed in the UI"""
     doc_id_to_saved_doc_id_map: dict[str, int] = {}
@@ -117,7 +121,7 @@ def translate_citations(
                 citation.citation_num
             ] = doc_id_to_saved_doc_id_map[citation.document_id]
 
-    return citation_to_saved_doc_id_map
+    return MessageSpecificCitations(citation_map=citation_to_saved_doc_id_map)
 
 
 def _handle_search_tool_response_summary(
@@ -239,11 +243,14 @@ def _get_force_search_settings(
     StreamingError
     | QADocsResponse
     | LLMRelevanceFilterResponse
+    | FinalUsedContextDocsResponse
     | ChatMessageDetail
     | DanswerAnswerPiece
+    | AllCitations
     | CitationInfo
     | ImageGenerationDisplay
     | CustomToolResponse
+    | MessageSpecificCitations
     | MessageResponseIDInfo
 )
 ChatPacketStream = Iterator[ChatPacket]
@@ -688,9 +695,13 @@ def stream_chat_message_objects(
                             )
 
                         yield LLMRelevanceFilterResponse(
-                            relevant_chunk_indices=llm_indices
+                            llm_selected_doc_indices=llm_indices
                         )
 
+                elif packet.id == FINAL_CONTEXT_DOCUMENTS_ID:
+                    yield FinalUsedContextDocsResponse(
+                        final_context_docs=packet.response
+                    )
                 elif packet.id == IMAGE_GENERATION_RESPONSE_ID:
                     img_generation_response = cast(
                         list[ImageGenerationResponse], packet.response
@@ -743,12 +754,13 @@ def stream_chat_message_objects(
 
     # Post-LLM answer processing
     try:
-        db_citations = None
+        message_specific_citations: MessageSpecificCitations | None = None
         if reference_db_search_docs:
-            db_citations = translate_citations(
+            message_specific_citations = _translate_citations(
                 citations_list=answer.citations,
                 db_docs=reference_db_search_docs,
             )
+            yield AllCitations(citations=answer.citations)
 
         # Saving Gen AI answer and responding with message info
         tool_name_to_tool_id: dict[str, int] = {}
@@ -765,7 +777,9 @@ def stream_chat_message_objects(
             reference_docs=reference_db_search_docs,
             files=ai_message_files,
             token_count=len(llm_tokenizer_encode_func(answer.llm_answer)),
-            citations=db_citations,
+            citations=message_specific_citations.citation_map
+            if message_specific_citations
+            else None,
             error=None,
             tool_calls=[
                 ToolCall(

backend/danswer/danswerbot/slack/handlers/handle_regular_answer.py

@@ -419,7 +419,7 @@ def _get_answer(new_message_request: DirectQARequest) -> OneShotQAResponse | Non
     )
 
     # Get the chunks fed to the LLM only, then fill with other docs
-    llm_doc_inds = answer.llm_chunks_indices or []
+    llm_doc_inds = answer.llm_selected_doc_indices or []
     llm_docs = [top_docs[i] for i in llm_doc_inds]
     remaining_docs = [
         doc for idx, doc in enumerate(top_docs) if idx not in llm_doc_inds

backend/danswer/llm/answering/answer.py

@@ -52,7 +52,7 @@
 from danswer.tools.internet_search.internet_search_tool import InternetSearchTool
 from danswer.tools.message import build_tool_message
 from danswer.tools.message import ToolCallSummary
-from danswer.tools.search.search_tool import FINAL_CONTEXT_DOCUMENTS
+from danswer.tools.search.search_tool import FINAL_CONTEXT_DOCUMENTS_ID
 from danswer.tools.search.search_tool import SEARCH_DOC_CONTENT_ID
 from danswer.tools.search.search_tool import SEARCH_RESPONSE_SUMMARY_ID
 from danswer.tools.search.search_tool import SearchResponseSummary
@@ -433,7 +433,7 @@ def _raw_output_for_non_explicit_tool_calling_llms(
         if tool.name in {SearchTool._NAME, InternetSearchTool._NAME}:
             final_context_documents = None
             for response in tool_runner.tool_responses():
-                if response.id == FINAL_CONTEXT_DOCUMENTS:
+                if response.id == FINAL_CONTEXT_DOCUMENTS_ID:
                     final_context_documents = cast(list[LlmDoc], response.response)
                 yield response
 
@@ -501,12 +501,10 @@ def _process_stream(
             message = None
 
             # special things we need to keep track of for the SearchTool
-            search_results: list[
-                LlmDoc
-            ] | None = None  # raw results that will be displayed to the user
-            final_context_docs: list[
-                LlmDoc
-            ] | None = None  # processed docs to feed into the LLM
+            # raw results that will be displayed to the user
+            search_results: list[LlmDoc] | None = None
+            # processed docs to feed into the LLM
+            final_context_docs: list[LlmDoc] | None = None
 
             for message in stream:
                 if isinstance(message, ToolCallKickoff) or isinstance(
@@ -525,8 +523,9 @@ def _process_stream(
                                 SearchResponseSummary, message.response
                             ).top_sections
                         ]
-                    elif message.id == FINAL_CONTEXT_DOCUMENTS:
+                    elif message.id == FINAL_CONTEXT_DOCUMENTS_ID:
                         final_context_docs = cast(list[LlmDoc], message.response)
+                        yield message
 
                     elif (
                         message.id == SEARCH_DOC_CONTENT_ID

backend/danswer/one_shot_answer/answer_question.py

@@ -371,7 +371,7 @@ def get_search_answer(
         elif isinstance(packet, QADocsResponse):
             qa_response.docs = packet
         elif isinstance(packet, LLMRelevanceFilterResponse):
-            qa_response.llm_chunks_indices = packet.relevant_chunk_indices
+            qa_response.llm_selected_doc_indices = packet.llm_selected_doc_indices
         elif isinstance(packet, DanswerQuotes):
             qa_response.quotes = packet
         elif isinstance(packet, CitationInfo):

backend/danswer/one_shot_answer/models.py

@@ -62,7 +62,7 @@ class OneShotQAResponse(BaseModel):
     quotes: DanswerQuotes | None = None
     citations: list[CitationInfo] | None = None
     docs: QADocsResponse | None = None
-    llm_chunks_indices: list[int] | None = None
+    llm_selected_doc_indices: list[int] | None = None
     error_msg: str | None = None
     answer_valid: bool = True  # Reflexion result, default True if Reflexion not run
     chat_message_id: int | None = None

backend/danswer/server/utils.py

@@ -1,9 +1,31 @@
 import json
+from datetime import datetime
 from typing import Any
 
 
-def get_json_line(json_dict: dict) -> str:
-    return json.dumps(json_dict) + "\n"
+class DateTimeEncoder(json.JSONEncoder):
+    """Custom JSON encoder that converts datetime objects to ISO format strings."""
+
+    def default(self, obj: Any) -> Any:
+        if isinstance(obj, datetime):
+            return obj.isoformat()
+        return super().default(obj)
+
+
+def get_json_line(
+    json_dict: dict[str, Any], encoder: type[json.JSONEncoder] = DateTimeEncoder
+) -> str:
+    """
+    Convert a dictionary to a JSON string with datetime handling, and add a newline.
+
+    Args:
+        json_dict: The dictionary to be converted to JSON.
+        encoder: JSON encoder class to use, defaults to DateTimeEncoder.
+
+    Returns:
+        A JSON string representation of the input dictionary with a newline character.
+    """
+    return json.dumps(json_dict, cls=encoder) + "\n"
 
 
 def mask_string(sensitive_str: str) -> str:

backend/danswer/tools/internet_search/internet_search_tool.py

@@ -20,7 +20,7 @@
 from danswer.secondary_llm_flows.query_expansion import history_based_query_rephrase
 from danswer.tools.internet_search.models import InternetSearchResponse
 from danswer.tools.internet_search.models import InternetSearchResult
-from danswer.tools.search.search_tool import FINAL_CONTEXT_DOCUMENTS
+from danswer.tools.search.search_tool import FINAL_CONTEXT_DOCUMENTS_ID
 from danswer.tools.tool import Tool
 from danswer.tools.tool import ToolResponse
 from danswer.utils.logger import setup_logger
@@ -224,7 +224,7 @@ def run(self, **kwargs: str) -> Generator[ToolResponse, None, None]:
         ]
 
         yield ToolResponse(
-            id=FINAL_CONTEXT_DOCUMENTS,
+            id=FINAL_CONTEXT_DOCUMENTS_ID,
             response=llm_docs,
         )
 

backend/danswer/tools/search/search_tool.py

@@ -45,7 +45,7 @@
 SEARCH_RESPONSE_SUMMARY_ID = "search_response_summary"
 SEARCH_DOC_CONTENT_ID = "search_doc_content"
 SECTION_RELEVANCE_LIST_ID = "section_relevance_list"
-FINAL_CONTEXT_DOCUMENTS = "final_context_documents"
+FINAL_CONTEXT_DOCUMENTS_ID = "final_context_documents"
 SEARCH_EVALUATION_ID = "llm_doc_eval"
 
 
@@ -179,7 +179,7 @@ def build_tool_message_content(
         self, *args: ToolResponse
     ) -> str | list[str | dict[str, Any]]:
         final_context_docs_response = next(
-            response for response in args if response.id == FINAL_CONTEXT_DOCUMENTS
+            response for response in args if response.id == FINAL_CONTEXT_DOCUMENTS_ID
         )
         final_context_docs = cast(list[LlmDoc], final_context_docs_response.response)
 
@@ -260,7 +260,7 @@ def _build_response_for_specified_sections(
             for section in final_context_sections
         ]
 
-        yield ToolResponse(id=FINAL_CONTEXT_DOCUMENTS, response=llm_docs)
+        yield ToolResponse(id=FINAL_CONTEXT_DOCUMENTS_ID, response=llm_docs)
 
     def run(self, **kwargs: str) -> Generator[ToolResponse, None, None]:
         query = cast(str, kwargs["query"])
@@ -343,12 +343,12 @@ def run(self, **kwargs: str) -> Generator[ToolResponse, None, None]:
             llm_doc_from_inference_section(section) for section in pruned_sections
         ]
 
-        yield ToolResponse(id=FINAL_CONTEXT_DOCUMENTS, response=llm_docs)
+        yield ToolResponse(id=FINAL_CONTEXT_DOCUMENTS_ID, response=llm_docs)
 
     def final_result(self, *args: ToolResponse) -> JSON_ro:
         final_docs = cast(
             list[LlmDoc],
-            next(arg.response for arg in args if arg.id == FINAL_CONTEXT_DOCUMENTS),
+            next(arg.response for arg in args if arg.id == FINAL_CONTEXT_DOCUMENTS_ID),
         )
         # NOTE: need to do this json.loads(doc.json()) stuff because there are some
         # subfields that are not serializable by default (datetime)